7.1 Instruction-level parallelism (ILP)

Instruction-level parallelism (ILP) is a key technique in modern processors. It allows multiple instructions to be executed simultaneously, boosting performance by making better use of processor resources. This approach is crucial for squeezing more speed out of single-core designs.

However, ILP isn't without limits. Data dependencies, control issues, and resource constraints can all hamper parallel execution. Designers must carefully balance the benefits of ILP against increased complexity and power consumption to create efficient processors.

Instruction-level Parallelism for Performance

Concept and Benefits

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• Instruction-level parallelism (ILP) enables a processor to execute multiple instructions simultaneously or in parallel within a single processing core
• ILP increases the utilization of processor resources and improves overall performance by exploiting the inherent parallelism present in instruction streams
• The degree of achievable ILP depends on the independence of instructions, the availability of processor resources, and the ability to efficiently schedule and execute instructions in parallel
• Techniques such as pipelining, superscalar execution, and out-of-order execution are employed to exploit ILP and enhance processor performance (superscalar execution, out-of-order execution)

Limitations and Dependencies

• ILP is limited by various types of dependencies, such as data dependencies, control dependencies, and resource dependencies, which restrict the parallelism that can be achieved
• Data dependencies occur when an instruction depends on the result of a previous instruction, requiring the instructions to be executed in a specific order to maintain correctness (read-after-write, write-after-write, write-after-read)
• Control dependencies are caused by branch instructions, where the execution path is determined by the outcome of the branch, limiting the ability to execute instructions in parallel across the branch boundary
• Resource dependencies arise when instructions compete for the same processor resources, such as functional units, registers, or memory ports, leading to resource conflicts and stalls in the pipeline
• Structural dependencies stem from the physical limitations of the processor hardware, such as the number of available functional units or the size of the reorder buffer, restricting the amount of exploitable parallelism

Dependencies Limiting Parallelism

Data Dependencies

• True data dependencies (read-after-write) occur when an instruction requires the result of a previous instruction as its input operand
• Output dependencies (write-after-write) arise when two instructions write to the same register or memory location, and the order of writes must be preserved
• Anti-dependencies (write-after-read) happen when an instruction writes to a register or memory location that a previous instruction has read, potentially overwriting the value before it is used
• Data dependencies enforce a specific execution order to maintain correctness and limit the parallelism that can be achieved (true dependency, output dependency, anti-dependency)

Control and Resource Dependencies

• Control dependencies are caused by branch instructions, where the execution path is determined by the outcome of the branch, limiting the ability to execute instructions in parallel across the branch boundary
• Branch prediction techniques, such as static or dynamic prediction, are used to speculatively execute instructions beyond the branch, but incorrect predictions can lead to pipeline flushes and performance penalties (branch misprediction)
• Resource dependencies occur when instructions compete for the same processor resources, such as functional units, registers, or memory ports, leading to resource conflicts and stalls in the pipeline
• Structural dependencies arise from the physical limitations of the processor hardware, such as the number of available functional units or the size of the reorder buffer, restricting the amount of parallelism that can be exploited (functional unit contention, register file port contention)

Techniques for Exploiting Parallelism

Pipelining

• Pipelining divides the execution of instructions into multiple stages, allowing multiple instructions to be in different stages of execution simultaneously
• Instruction pipelining overlaps the fetch, decode, execute, memory access, and write-back stages of instructions, enabling increased throughput and parallelism (5-stage pipeline)
• Pipeline hazards, such as data hazards, control hazards, and structural hazards, can disrupt the smooth flow of instructions through the pipeline and limit the achievable ILP
• Techniques like data forwarding, branch prediction, and pipeline stalls are used to mitigate pipeline hazards and maintain pipeline efficiency (data forwarding, branch prediction, pipeline stalls)

Out-of-Order Execution and Superscalar Execution

• Out-of-order execution allows instructions to be executed in an order different from the original program order, based on their dependencies and the availability of resources
• Instructions are dynamically scheduled based on their readiness, allowing independent instructions to be executed in parallel, even if they appear later in the program order (dynamic scheduling)
• Out-of-order execution requires hardware mechanisms such as reservation stations, reorder buffers, and register renaming to track dependencies, maintain correct execution order, and handle exceptions (reservation stations, reorder buffer, register renaming)
• Superscalar execution involves multiple instructions being issued, executed, and completed simultaneously, using multiple parallel pipelines or functional units
• Superscalar processors have the ability to fetch, decode, and dispatch multiple instructions per clock cycle, exploiting both instruction-level and pipeline-level parallelism (4-way superscalar, 8-way superscalar)
• Dynamic scheduling and out-of-order execution are often employed in superscalar processors to maximize the utilization of parallel execution resources

Parallelism vs Design Considerations

Complexity and Power Consumption

• Exploiting higher levels of ILP often comes at the cost of increased processor complexity, as more hardware resources and control logic are required to support parallel execution
• Complex out-of-order execution engines, larger reorder buffers, and more functional units contribute to the overall complexity of the processor design (out-of-order execution engine, reorder buffer)
• Increased complexity can lead to longer design and verification cycles, higher development costs, and potential challenges in maintaining processor correctness and reliability
• Achieving higher ILP typically requires more power-hungry hardware structures and higher clock frequencies, leading to increased power consumption and heat dissipation
• The power consumed by the processor grows with the level of parallelism exploited, as more transistors are active simultaneously, and the switching activity increases (dynamic power consumption)

Balancing Performance and Costs

• Power management techniques, such as clock gating, power gating, and dynamic voltage and frequency scaling (DVFS), are employed to mitigate the power overhead of ILP exploitation (clock gating, power gating, DVFS)
• The diminishing returns of ILP at higher levels of parallelism pose a challenge in balancing performance gains with the associated costs and complexity
• Beyond a certain point, the performance benefits of increasing ILP may not justify the additional hardware resources, power consumption, and design complexity required (diminishing returns)
• Other factors, such as memory latency, cache performance, and the inherent parallelism of the application, can become the bottlenecks limiting overall system performance (memory latency, cache performance)
• The trade-off between ILP and other design considerations requires careful analysis and optimization based on the target application domain, power budget, and performance requirements
• Different processor designs may prioritize ILP differently, depending on their intended use cases, such as high-performance computing, energy-efficient mobile devices, or real-time embedded systems (application-specific optimization)
• Techniques like dynamic ILP adaptation, where the processor can adjust its parallelism level based on workload characteristics and power constraints, can help strike a balance between performance and other design goals (dynamic ILP adaptation)